Department of Radiology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, 100053, Beijing, China.
Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, No. 45 Changchun Street, Xicheng District, 100053, Beijing, China.
Nat Commun. 2020 Sep 24;11(1):4829. doi: 10.1038/s41467-020-18606-2.
The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care.
计算机断层血管造影术 (CTA) 后处理由技术人员手动识别非常耗费人力且容易出错。我们提出了一种人工智能重建系统，该系统得到了经过优化的基于生理解剖结构的三维卷积神经网络的支持，可以在医疗服务中自动实现 CTA 重建。该系统使用 2017 年 6 月至 2018 年 11 月期间从中国的 5 家三级医院收集的 18766 例头颈部 CTA 扫描进行了训练和测试。独立测试数据集的整体重建准确性为 0.931。由于与手动处理的图像具有一致性，因此它具有临床适用性，合格率达到 92.1%。该系统将耗时从 14.22±3.64 分钟减少到 4.94±0.36 分钟，点击次数从 115.87±25.9 减少到 4，并且在五个月的应用后，人力从 3 名技术人员减少到 1 名。因此，该系统简化了临床工作流程，并为临床技术人员提供了改善人性化患者护理的机会。
